On Sep 4, 2010, at 4:18 PM, Sally Luo wrote:
Hi R users,
I am using the optim funciton to maximize a log likelihood
function. My
code is as follows:
p<-optim(c(-0.2392925,0.4653128,-0.8332286, 0.0657, -0.0031,
-0.00245,
3.366, 0.5885, -0.00008,
0.0786,-0.00292,-0.00081, 3.266, -0.3632, -0.000049,
0.1856,
0.00394, -0.00193, -0.889, 0.5379, -0.000063,
0.213, 0.00338, -0.00026, -0.8912, -0.3023, -0.000056), f,
method
="BFGS", hessian =TRUE, y=y,X=X,W=W)
After I ran the code, I got the following results:
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p
$par
[1] 2.235834e-02 1.282826e-01 -3.786014e-01 7.422526e-02
3.037931e-02
-2.570156e-03 3.365872e+00 2.618893e-01 -1.987859e-06
[10] 7.970083e-02 2.878574e-03 -1.391019e-03 3.265966e+00
-4.153697e-01
-3.185684e-03 1.833200e-01 -7.247683e-03 -3.156813e-03
[19] -8.889219e-01 6.208612e-01 2.678643e-04 2.183787e-01
2.715062e-02
2.943905e-04 -8.913260e-01 -5.100482e-01 -3.477559e-04
$value
[1] -932.1423
$counts
function gradient
1439 100
$convergence
[1] 1
$message
NULL
$hessian ( I omitted the approximation results for the hessian here
to save
space)
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The error code 1 for convergence shown above means that the
iteration limit
maxit had been reached. How can I fix this problem and achieve
convergence
for my optimization problem? Can I increase the number of maxit so
that
convergence might occur?
I am wondering how you expect us to guess at the answer? You are the
one who know what "f" is and you are the one who has the option of
increasing maxit. If the question is "how" to increase maxit, then the
answer is perhaps as easy as:
?optim
--
David Winsemius, MD
West Hartford, CT
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